Search Results (401)

Beer quality assessment increasingly requires rapid and scalable analytical tools for product discrimination and authenticity control. In this study, a data-driven metal oxide semiconductor (MOX) chemosensing approach was investigated for the discrimination of commercial lager beers with different alcohol contents and brands. Alcoholic and alcohol-free beer samples from four commercial brands were analyzed using a six-element SnO₂-based MOX sensor array, and the resulting response patterns were classified using supervised machine-learning algorithms. Headspace solid-phase microextraction gas chromatography–mass spectrometry (HS-SPME-GC–MS) was employed as a reference technique to characterize volatile organic compound profiles and support the interpretation of sensor-based fingerprints. GC–MS analysis highlighted a shared volatile backbone dominated by fermentation-related compounds, while also revealing brand- and category-dependent differences in VOC distribution. The MOX sensor array captured these differences as multidimensional volatile fingerprints. Machine-learning models achieved high classification performance in brand-matched alcoholic versus alcohol-free comparisons, with balanced accuracy ranging from 0.937 to 1.000, while brand discrimination within the same category reached balanced accuracy values of 0.875 (alcoholic) and 0.933 (alcohol-free). These results highlight MOX-based chemosensing combined with data-driven analysis as a rapid, portable platform for beer discrimination, with applications in food quality screening, authenticity assessment, and at-line monitoring. Full article

In response to the EURO 7 regulation, which mandates near-continuous monitoring of pollutant gas emissions from every vehicle during real driving conditions, this research reports the development of a micro portable emissions monitoring system (µPEMS) for monitoring tailpipe mass emissions of NO_x, CO, and CO₂. It consists of low-cost MOX sensors installed in the exhaust pipe to detect pollutant concentrations, complemented with engine operation data from the vehicle’s On-Board Diagnostics (OBD) system. Issues of sensor drift, cross-sensitivity, and varying sampling frequency were addressed. Readings from this µPEMS prototype exhibited high correlation (R² > 0.87) with experimental data obtained under real driving conditions using a well-accepted PEMS for the cases of three vehicles (gasoline, diesel, and hybrid). This innovation enables new alternatives to regulate vehicular emissions. It also provides valuable real-time data for improving ecodriving, vehicle technology, and national emission inventories. Full article

Landfills represent a significant source of methane emissions, with important environmental, climatic and safety impacts due to the widespread and variable nature of these emissions. Traditional monitoring methods, such as flow chambers coupled with flame ionisation detectors (FIDs), provide high accuracy but are limited in terms of spatial representativeness, operational flexibility and cost, especially during large-scale or continuous monitoring campaigns. Within this context, the European ESCAPE project aims to develop a low-cost, portable and modular platform for the detection and quantification of low methane concentrations in ambient air at complex environmental sites. The system is based on commercial MOX and NDIR sensors integrated into portable toolboxes equipped with dedicated chambers, regulated suction systems and autonomous data acquisition units with real-time transmission. This work describes the development and testing of two identical toolboxes to assess system reproducibility and the transferability of predictive models between devices. Laboratory and field tests were carried out under controlled and real landfill conditions, with comparisons against portable FID measurements. Results showed good agreement between predicted methane concentrations and reference data, with correlation indexes up to 0.77. Moreover, transferring the machine learning model between toolboxes did not produce statistically significant performance reductions, demonstrating promising robustness and generalizability of the proposed calibration strategy. Full article

Nowadays, the medical sector challenges young research teams to develop and propose new non-invasive diagnostic methods. As a potential response, gas sensors for biomarker detection in exhaled breath show promising results. In this paper, various gas sensors based on metal–oxide semiconductors and coated with different polymers are proposed, demonstrating the potential of these sensors in breathomics and health breath tests. The proposed sensors are based on TiO₂ sensing structures and are tuned through different methods. Furthermore, they are coated with polymers such as PV4D4, PTFE, PV3D3, and copolymers such as P(V3D3 + TFE). These polymers show improved efficiency for gas sensing structures as they act as filters for certain molecules. Full article

The use of insect-based protein sources in aquaculture is gaining increasing attention with Hermetia illucens (black soldier fly, BSF) larvae meal representing a promising substitute to fishmeal (FM). This study evaluated the effect of partial dietary inclusion of BSF meal (BSF0, BSF2.5, BSF5, BSF10%) on the volatilome of rainbow trout (Oncorhynchus mykiss) fillets, before and after cooking, using gas chromatography–mass spectrometry (GC–MS) and a metal oxide sensor-(MOX)-based device. Fish were fed diets with increasing BSF inclusion, and both raw and cooked fillets were analyzed to assess changes in volatile organic compounds (VOCs). GC–MS enabled the identification and semi-quantitative analysis of VOC classes, while MOX sensor responses were processed using Linear Discriminant Analysis (LDA) to assess discrimination among dietary treatments. Results showed that BSF inclusion influenced the volatile profile, with clearer separation at higher inclusion levels (BSF5–BSF10%), especially in cooked fillets. Thermal processing enhanced these differences. GC–MS analysis revealed a reduction in aldehydes and ketones and an increase in carboxylic acids with higher BSF inclusion. Key compounds such as hexanal and heptanal decreased, indicating changes in lipid-derived volatile pathways. Overall, the integration of GC–MS and MOX sensors proved effective in detecting diet-induced changes, supporting their application as effective and reliable tools for quality assessment in aquaculture products, with potential implications for sensory quality that should be further confirmed through dedicated sensory studies. Full article

Indoor monitoring of biological contamination is essential for protecting cultural heritage and public health. However, conventional culture-based methods limit timely intervention. This study presents an affordable modular multisensor system for indirectly detecting airborne fungal contamination using Penicillium chrysogenum as a representative model organism and its environmental signatures. The proposed prototype integrates PMSA003I, BME688 and AMG8833 sensors and was evaluated under controlled environmental conditions. Biological ground truth was established using a volumetric inertial-impaction sampling protocol (SAS sampler), validating four contamination levels (~6 to 165, CFU/m³). A total of 1989 observations were analyzed. Non-parametric statistical tests (Kruskal–Wallis and Mann–Whitney U) confirmed significant differences between all the exposure conditions ($p<0.001$). Supervised machine learning (ML) models showed strong performance across all the classification tasks, with accuracy and AUC values near 100%. In most cases, pressure alone was sufficient. The statistical and ML analyses consistently identified pressure, particulate-related variables, gas resistance and humidity as the most informative features. Overall, the results indicate that the proposed approach can reliably capture indirect environmental signatures associated with airborne fungal presence under controlled conditions. The study supports the feasibility of low-cost multisensor systems for continuous indoor bioaerosol monitoring while highlighting the need for further optimization and validation in real-world environments. Full article

A portable electronic nose based on modern digital metal oxide (MOX) gas sensors and programmable temperature modulation was developed and validated. The system integrates four modern commercially available MOX sensors capable of generating temperature-dependent odor fingerprints and multidimensional sensor responses compared with conventional fixed-temperature operation. The performance of the device was assessed in terms of sensor stability, repeatability, and pattern-recognition capability under long-term operation. As a proof of concept, the electronic nose was applied to the discrimination of Extra Virgin Olive Oil and pomace oil. Repeatability analysis using the Root Mean Squared Error (RMSE) demonstrated stable responses across one month of measurements. Temperature-modulated signals were processed using Principal Component Analysis (PCA) and classified with k-Nearest Neighbors (KNNs) and Multilayer Perceptrons (MLPs), achieving 100% accuracy after selecting the most repeatable sensor. These results highlight the robustness and analytical potential of temperature-modulated digital MOX sensors and demonstrate the feasibility of a compact and highly reproducible electronic-nose platform suitable for complex odor-analysis tasks in real-world applications. Full article

Moxifloxacin-based Schiff-base ligands provide a useful platform for tuning the coordination and biological properties of fluoroquinolone derivatives. Here, a moxifloxacin–salicylaldehyde hydrazone ligand (MOX-S) was prepared and coordinated with cobalt(II), nickel(II), copper(II), oxovanadium(IV), and gadolinium(III) ions to obtain a series of metal complexes. Citrate-stabilized gold nanoparticles (AuNPs) were also prepared and functionalized with MOX-S and the Cu(II) complex to evaluate the effect of nanoformulation on biological performance. The compounds were characterized using complementary analytical, spectroscopic, magnetic, thermal, and microscopic techniques. The combined data support 1:2 metal-to-ligand formulations for the complexes and indicate coordination mainly through the azomethine nitrogen and oxygen donor sites of MOX-S. In antimicrobial screening, the activity was strongly metal- and organism-dependent. Cu–MOX-S and VO–MOX-S showed the most pronounced activity against Gram-positive bacteria, with inhibition zones of up to 30 mm, while Cu–MOX-S displayed MIC values of 19.53 and 39.06 µg mL⁻¹ against Bacillus subtilis and Staphylococcus aureus, respectively. Cytotoxicity assays showed that MOX-S was more active than moxifloxacin against MCF-7 and HepG2 cells, while Cu–MOX-S showed enhanced potency, particularly toward HepG2 cells, with an IC₅₀ of 0.98 µM and a selectivity index of 5.97. AuNP formulations further increased the apparent antiproliferative potency in the tested cancer cell lines, giving sub-micromolar IC₅₀ values. Computational analyses, including DFT-based electronic descriptors and molecular docking, provided qualitative support for the experimentally observed coordination and cytotoxicity trends. Overall, metal coordination and AuNP formulations provide complementary strategies for modulating the physicochemical and in vitro biological behavior of this moxifloxacin-derived hydrazone scaffold. Full article

Background/Objectives: A short scarf osteotomy involves the conventional “Z”-shaped osteotomy which is shorter in length with promising results. Fixation can be performed with a single screw or two screws. This observational study evaluated the clinical effectiveness of single-screw versus traditional two-screw scarf osteotomies for the correction of hallux valgus deformities. Methods: Forty-eight hallux valgus cases treated between 2019 and 2024 were reviewed. Data collected included patient demographics, operative details, radiological outcomes, and patient-reported outcome measures (PROMs). PROMs were assessed using the Manchester Oxford Foot Questionnaire (MOxFQ) and the visual analogue scale (VAS). A cost analysis was also performed. Results: Fourteen patients (29.2%) underwent traditional two-screw scarf osteotomies. In this group, the hallux valgus angle (HVA) improved from 37.4° to 19.6° (p < 0.001), and the intermetatarsal angle (IMA) improved from 16.5° to 8.9° (p = 0.004). MOxFQ scores decreased from 37.8 to 27.4 (p = 0.139), and VAS scores improved from 7.4 to 3.3 (p = 0.012). Complications were limited to two superficial infections (14.3%). The remaining 34 patients (70.8%) underwent modified single-screw scarf osteotomies. This group demonstrated an HVA improvement from 34.7° to 12.6° (p < 0.001) and an IMA improvement from 15.5° to 7.8° (p < 0.001). MOxFQ scores improved from 39.4 to 10.4 (p < 0.001), and VAS scores decreased from 6.6 to 2.5 (p = 0.002). Complications included three superficial infections (8.8%), two intraoperative fractures (5.9%), and two cases of complex regional pain syndrome (5.9%). Conclusions: The modified single-screw scarf osteotomy appears clinically non-inferior to the traditional two-screw technique. Although wider validation is required, these findings suggest that the single-screw technique may offer an alternative that maintains clinical outcomes while supporting more sustainable healthcare resource use. Full article

Electronic nose platforms based on metal-oxide (MOX) sensors offer potential for low-power gas classification under dynamic operating conditions. This study evaluates a BME688-based digital nose configured with a temperature-modulated heater profile (HP-354) and reduced duty cycle (RDC-5-10) for binary ethylene presence classification in fruit headspace. Seven climacteric fruit types were sealed in bags to allow natural ethylene accumulation and were sampled across multiple sessions over a two-week period. A structured alternating protocol between fruit headspace (Class A) and neutral air (Class B) generated 21 ethylene sessions and 23 neutral-air sessions, comprising 38,882 individual thermal scan cycles (~10 s per cycle). Each full heater cycle was treated as a training instance within BME AI-Studio. A supervised neural-network classifier trained on 70% of cycle-level data achieved 92.9% overall accuracy with a macro F1 score of 91.9% on validation data. Results demonstrate that temperature-modulated MOX signatures enable robust discrimination of biologically generated ethylene from baseline air under realistic headspace variability. This study demonstrated classification feasibility under naturally accumulated fruit emissions while highlighting the need for future concentration-resolved calibration studies. Full article

We analyse pebble RFID tracing observations to investigate sediment transport dynamics in gravel-bed rivers using statistical modelling. This study examines a dataset of nearly 3500 tracer displacement measurements collected during 27 sediment-mobilizing events in a pre-Alpine reach in Italy. Our analysis follows three main steps, addressing tracer mobility patterns, event-scale transport dynamics, and reach-scale bedload inference. First, using Markov Chain analysis of state transitions on typical and high-magnitude transport events, we demonstrate that pebbles tend to maintain their mobility state between events, characterizing the between-event intermittency of bedload transport. A subsequent analysis of flow characteristics reveals that consecutive floods of similar magnitude exhibit increasing movement probability while maintaining similar virtual velocities. Finally, we train Gradient Boosting regression models to estimate distributions of pebble displacements and virtual velocities (defined, following common usage, as the ratio between the distance a tracer travels during a mobilising event and the duration of that event). Together with Monte Carlo propagation, these models are used to derive reach-scale volume estimates. The models identify flow rate and event duration as primary controls, while grain size has minimal influence within the sampled range of tracer dimensions. To strengthen our approach, we implement an extensive multi-stage validation process aimed at both single-tracer predictions and overall basin-scale movement estimates. The results indicate that high-magnitude transport events (12% of observations) contribute similar bedload volumes as typical events (88% of observations), highlighting the significant role of extreme events in total sediment transport. Model predictions yield bedload volume estimates that align well with independent measurements from a downstream sediment retention basin. Full article

Core physics multigroup cross-section libraries provide essential cross-section and burnup data for reactor neutron physics calculations, serving as a fundamental prerequisite for reactor physics analysis. The China Nuclear Data Center has developed the TPEX multigroup cross-section library for pressurized water reactors (PWRs) based on the Chinese Evaluated Nuclear Data Library CENDL-3.2. A systematic critical benchmark validation of the newly developed TPEX library has been performed. To verify its applicability and accuracy, the validation has been conducted against 131 critical benchmark experiments from the International Criticality Safety Benchmark Evaluation Project (ICSBEP 2006) and the WIMS-D library update project. The calculated effective multiplication factors $\left(k_{eff}\right)$ are compared with the experimental values, results from equivalent multigroup libraries, and reference solutions from Monte Carlo code. The results indicate that the absolute average deviations between the calculated $k_{eff}$ values using the TPEX library and the experimental measurements are 280 pcm for the uranium solution experiments, 410 pcm for the plutonium solution experiments, 10 pcm for the uranium metal lattice experiments, 20 pcm for the uranium dioxide lattice experiments, 22 pcm for the MOX fuel lattice experiments, and 150 pcm for the LCT001 uranium oxide assembly experiments. Accordingly, the TPEX library demonstrates excellent performance in reactivity predictions for PWRs. Full article

Odour recognition with low-cost gas sensors is challenging in open-air settings due to the non-specificity of the sensors and environmental variability. This can be mitigated by incorporating additional information into the classification process. This paper investigates odour-image multimodality in two case-studies of increasing complexity: banana ripening in open-air environment and strawberry ripening in a glasshouse environment. Data were collected using custom acquisition platforms equipped with cameras and MOX gas sensors operated with temperature modulation. For the visual modality, image classification (MobileNetV3) and object detection (YoloV5) models are trained. For the odour modality, established classical machine learning methods (Random Forest, XGBoost, SVM and Logistic Regression) and neural networks (1D-CNN, LSTM, MLP, and ELM) are employed. Each modality is analysed independently and together to critically assess scenarios in which combining modalities provides a clear advantage over using either modality alone. Results show that models trained on odour data achieve high accuracy in controlled environments but underperform in more dynamic open-air settings. Image-based models are sensitive to the image quality in all environments; however, they are more robust when deployed in different environments. Lastly, it is demonstrated that decision fusion consistently increases the accuracy, by as much as +12.36% in the banana ripening and +3.63% in the strawberry ripening scenario. Where decision fusion does not improve classification accuracy significantly, it is shown that the multimodal approach can still be leveraged to identify high-confidence predictions by selecting samples where both modalities agree on the label. Full article

The traditional solid-state method was employed in this study to prepare Mn-Zn ferrite. By adjusting the sintering temperature and the MoO₃ doping ratio, the evolution of its structural and magnetic properties was systematically investigated. Fe₂O₃, MnO, and ZnO were used as the main raw materials, with MoO₃ serving as an additive. MoO₃ was doped at molar ratios ranging from 0 to 1000 ppm under experimental conditions involving a sintering temperature between 1125 °C and 1165 °C and an oxygen concentration of 1.5%. The addition of an appropriate amount of MoO₃ led to an increase in the Q value, which consequently resulted in a reduction in the loss. The formation of a single-phase spinel structure was confirmed by X-ray diffraction analysis. Observations of the surface morphology revealed that the grain size also increased with the increase in MoO₃ content, a trend consistent with the enhanced grain growth kinetics at higher MoO₃ levels. In this study, a Mn-Zn ferrite material with excellent comprehensive performance was successfully prepared under the optimal conditions of a sintering temperature of 1150 °C and a MoO₃ doping concentration of 500 ppm. A Q value of 22.3 was obtained for this material at 25 °C, while a Q value of 15.7 was obtained at 100 °C. At room temperature, a Q value of 192.4 was measured at a test frequency of 500 kHz, and a Q value of 137.2 was measured at 1 MHz. At a frequency of 500 kHz, a loss of 27.1 kW/m³ was observed at 25 °C, and a loss of 53.6 kW/m³ was observed at 100 °C. At a frequency of 1 MHz, a loss of 88.2 kW/m³ was recorded at 25 °C, while a loss of 183.7 kW/m³ was recorded at 100 °C. Additionally, the lattice constant was stabilized in the range of 8.52–8.53 Å, indicating favorable structural stability. Full article

Emission control of diesel particulate matter (soot) combustion is important for environmental reasons. Catalysts are indispensable for optimizing these processes, as they significantly reduce the combustion temperature. In this work, mixed oxides (cerium–copper, cerium–manganese, and cerium–molybdenum) were prepared by co-precipitation under reasonably similar synthesis conditions, and the effects of their chemical composition on diesel soot combustion were evaluated using the Printex U model particulate. Thermogravimetric analysis (TG/DTG) and temperature-programmed oxidation coupled with mass spectrometry (TPO/MS) were employed for activity characterization. Structural analyses revealed the presence of nanocrystalline phases containing CeO₂ (fluorite), CuO (monoclinic), Mn₂O₃ (cubic), and MoO₃ (orthorhombic), depending on the catalyst composition. The most effective catalysts exhibited an equimolar oxide composition (CeO₂–MO_x). Tests performed at optimized calcination temperatures and with the addition of promoters led to the identification of optimal combustion conditions. The highest activity, corresponding to the lowest combustion temperature, was observed in the following order: CeO₂–Mn₂O₃ > CeO₂–CuO > CeO₂–MoO₃, with values of 382, 409, and 425 °C, respectively, under tight-contact conditions at a Printex U:catalyst ratio of 1:20. With the addition of a 10% Ag₂O promoter, the CeO₂–Mn₂O₃ catalyst further reduced the oxidation temperature to 376 °C. Reusability tests generally indicated a 10–20% decrease in catalytic activity by the third reaction cycle. Full article